Poster 354: Tibiofemoral Bone Shape Features Are Associated with Re-Tear after Anterior Cruciate Ligament Reconstruction

Abstract

Introduction: Knee bony morphology is linked to anterior cruciate ligament (ACL) injury, ACL graft failure, and osteoarthritis (OA) risk, and employing tools such as statistical shape modeling could identify individuals prone to these joint conditions. Re-tear after ACL reconstruction remains a common and devastating complication, and therefore, recognizing patients at high risk of graft failure before primary ACL reconstruction is crucial for potential intervention to reduce failure rates. Magnetic resonance imaging (MRI) is most commonly obtained in the setting of possible ACL injury and before ACL reconstruction surgery, yet currently available MRI-based bone shape assessment methods require high-resolution sequences or manual segmentation, limiting feasibility of large-scale analysis. The development of automated tools for bone shape extraction from clinical MRI could allow for three-dimensional statistical shape modeling across large datasets. We established an automated pipeline for bone segmentation from proton density (PD)-weighted MRI scans, aiming to extract knee bony morphology from preoperative clinical MRIs in patients with ACL injuries. We hypothesized that our segmentation pipeline could uncover specific tibiofemoral joint bone shape features associated with ACL re-tear after reconstruction. Objective: To identify bone shape features prior to initial primary ACL reconstruction in patients with eventual ACL graft re-tear compared to those with a known, intact ACL graft. Methods: Patients who retrospectively experienced a subsequent re-tear after reconstruction within three years of initial injury were compared to those without a re-tear within three years (20 without subsequent re-tear, 22 with subsequent re-tear for distal femur; 22 without subsequent re-tear, 19 with subsequent re-tear for proximal tibia). The control group had MRI scans at 3 years after reconstruction that confirmed an intact ACL graft. An automatic femur and tibia segmentation framework was developed by training two deep convolutional neural networks using clinical, two-dimensional PD-weighted knee MRI sequences (3.5 mm slice thickness) from patients with ACL tears. Using the segmentation pipeline, bone was automatically segmented from preoperative clinical PD-weighted knee MRI sequence (3 mm slice thickness), three-dimensional triangulated meshes were generated, scaled to a reference, and aligned and matched to an atlas using Iterative Closest Point algorithm. Modes of shape variance were extracted from principal component analysis (PCA). The effect of each mode is modeled by three-dimensional surfaces for each specific shape feature. Modes were considered statistically significant if the mean principal component score between groups was p<0.05 using Students T-test. Results: Statistical shape modeling of the femoral condyles using 15 PCA modes reveals significant differences (p < 0.05) in Modes 1 and 4 at the initial injury in patients who subsequently experienced ACL re-tear after reconstruction compared to those without re-tear. Femur Mode 1 is characterized by a narrower intercondylar notch width, a wider medial condylar width, and an increased ratio of femoral condylar offset in patients with ACL re-tear. Femur Mode 4 illustrates a more prominent trochlear sulcus and increased surface area along the lateral femoral condyle relative to the medial condyle in patients with ACL re-tear. The initial 15 PCA modes of the distal femur collectively account for 75.04% of the normalized accumulated total variance. Statistical shape modeling of the tibial plateau with 15 PCA modes reveals that Mode 7 is significantly different (p < 0.05) at the initial injury in patients who experienced subsequent ACL re-tear after reconstruction compared to those without re-tear. Tibia Mode 7 indicates diminished ACL facet prominence, a squared lateral tibial plateau, and a broader, flatter tibial spine in patients with subsequent ACL re-tear. The first 15 PCA modes of the proximal tibia collectively explain 73.60% of the normalized accumulated total variance. Conclusions: Using an automatic bone segmentation pipeline on preoperative clinical MRIs, we identify specific shape features in the tibiofemoral joint associated with ACL re-tear after reconstruction. Patients with subsequent ACL re-tear exhibit a variety of distinct three-dimensional morphology differences, including reduced intercondylar notch width, wider medial condylar width, increased lateral tibial plateau convexity, and broader, lateralized tibial spine, uncovered using statistical shape modeling. Statistical shape modeling identifies three-dimensional morphology patterns, thus capturing differences otherwise unaccounted for by traditional two-dimensional measurements. This approach offers predictive potential for patients at risk of ACL re-tear, improving treatment decisions for those with high-risk morphologic features.